***Analysis Nicaragua

**load dataset
use "Nicaragua.dta", clear


**** forced choice
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)


set more off

*labor standards
reg country i.labor, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("NICARAGUA.txt") replace
matrix drop resmat


**graph
clear
set more off
insheet using "NICARAGUA.txt",  clear

gen varname=_n
list


#delimit;
#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;


#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2
list



#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off) scheme(s2mono);
#delimit cr

graph export "NICARAGUA_choice.pdf", replace





***Figure 2: overall support
use "Nicaragua.dta", clear
hist like, percent xlabel(1(1)7) ylabel(0(5)25) scheme(s2mono) xtitle("Support PTA with this partner 1-7 (1 no support)")

graph save "NICARAGUA_support.gph", replace








***Figure 3 - best and worst case scenario

**** Single model regression
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (single model regression)

use "Nicaragua.dta", clear

  #delimit;
regress like2 lab_sim lab_stro env_sim env_stro no_alli sem_dem dem rel_div 
rel_chri cult_dif econ_size_sim econ_size_large short middle, cl(respondent);
  #delimit cr

***predicted values

predict like3, xb


summ like3
**worst case
*Here the country is relatively far away (more than 10 000 km), is a small economy, 
*has lower labor and environmental standards, is not a military ally and not a democracy
*Islam predominant religion, and the country is not culturally similar 
*(language for Costa Rica and Nicaragua, or does not celebrate Lunar Year, for Vietnam).
  
  #delimit;
  sum like3 if lab_sim==0 & lab_stro==0 & env_sim==0 & env_stro==0 & econ_size_sim==0
  & econ_size_large==0 & no_alli==1 & sem_dem==0 & dem==0 & rel_div==0 &
  rel_chri==0 & cult_dif==1 & short==0 & middle==0;
  #delimit cr
 
   **best case
  *Here the potential trade partner is relatively close by (less than 1 000 km away), 
  *is a large economy, has stronger environmental and labor standards, is a military ally and a democracy, 
  *Christianity (Buddhism for Vietnam) is the predominant religion, and the country is culturally similar 
  *(language for Costa Rica and Nicaragua, or Lunar Year celebration for Vietnam

  #delimit;
  sum like3 if lab_sim==0 & lab_stro==1 & env_sim==0 & env_stro==1 & econ_size_sim==0
  & econ_size_large==1 & no_alli==0 & sem_dem==0 & dem==1 & rel_div==0 &
  rel_chri==1 & cult_dif==0 & short==1 & middle==0;
  #delimit cr
 


predict sylike3, stdp
gen lowerpred=like3-1.96*sylike3
gen upperpred=like3+1.96*sylike3

*worst case
#delimit;
summ upperpred lowerpred if lab_sim==0 & lab_stro==0 & env_sim==0 & env_stro==0 & econ_size_sim==0
  & econ_size_large==0 & no_alli==1 & sem_dem==0 & dem==0 & rel_div==0 &
  rel_chri==0 & cult_dif==1 & short==0 & middle==0;
  #delimit cr

  *best case
#delimit;
summ upperpred lowerpred if lab_sim==0 & lab_stro==1 & env_sim==0 & env_stro==1 & econ_size_sim==0
  & econ_size_large==1 & no_alli==0 & sem_dem==0 & dem==1 & rel_div==0 &
  rel_chri==1 & cult_dif==0 & short==1 & middle==0;
  #delimit cr

***These numbers are saved in Excel File "Scenarios"




*********Supplementary Material********************************************

**** rating task
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)
* dependent variable = like2

use "Nicaragua.dta", clear

capture matrix drop resmat

*labor standards
reg like2 i.labor, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg like2 i.env, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg like2 i.military, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg like2 i.democracy, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg like2 i.religion, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg like2 i.culture, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg like2 i.econ_size, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg like2 i.distance, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("Nicaragua_like.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_like.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;

#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2


#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.05(.05).15) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off);
#delimit cr



graph export "Nicaragua_like.pdf", replace


**** forced choice education high
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)


use "Nicaragua.dta", clear


*labor standards
reg country i.labor if education>4, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if education>4, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if education>4, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if education>4, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if education>4, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if education>4, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if education>4, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if education>4, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("Nicaragua_ed_high.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_ed_high.txt",  clear


gen varname=_n
list

clear matrix

capture label drop
#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;


#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2

#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), title(College) legend(off);
#delimit cr


graph export "NICARAGUA_ed_high.pdf", replace



**** forced choice education low
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)

use "Nicaragua.dta", clear


*labor standards
reg country i.labor if education<5, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if education<5, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if education<5, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if education<5, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if education<5, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if education<5, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if education<5, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if education<5, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("NICARAGUA_ed_low.txt") replace
matrix drop resmat


**graph

insheet using "NICARAGUA_ed_low.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;

#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2



#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), title(No college) legend(off);
#delimit cr


graph export "Nicaragua_ed_low.pdf", replace




**** forced choice income high
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)

use "Nicaragua.dta", clear

*labor standards
reg country i.labor if income>=4200, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if income>=4200, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if income>=4200, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if income>=4200, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if income>=4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if income>=4200, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if income>=4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if income>=4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}



mat2txt , matrix(resmat) saving("Nicaragua_income_high.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_income_high.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;


#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2


#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off);
#delimit cr


graph export "NICARAGUA_income_high.pdf", replace



**** forced choice income low
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)


use "Nicaragua.dta", clear


*labor standards
reg country i.labor if income<4200, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if income<4200, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if income<4200, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if income<4200, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if income<4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if income<4200, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if income<4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if income<4200, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("Nicaragua_income_low.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_income_low.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;

#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2

#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off);
#delimit cr

graph export "Nicaragua_income_low.pdf", replace




**** forced choice salience high
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)


use "Nicaragua.dta", clear

*labor standards
reg country i.labor if salience==1, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if salience==1, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if salience==1, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if salience==1, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if salience==1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if salience==1, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if salience==1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if salience==1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("Nicaragua_salience_high.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_salience_high.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;


#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2


#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off);
#delimit cr

graph export "Nicaragua_salience_high.pdf", replace



**** forced choice salience low
***results using linear estimator as suggested by Hainmueller et al.
** AMCEs: rating outcome with differences in means estimator (attribute by attribute)


use "Nicaragua.dta", clear

*labor standards
reg country i.labor if salience>1, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.labor 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.labor"
matrix resmat = nullmat(resmat) \ coef
}

*environmental standards
reg country i.env if salience>1, cl(respondent) 

foreach x of numlist 1(1)3 {
lincom  `x'.env
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.env"
matrix resmat = nullmat(resmat) \ coef
}
*alliance
reg country i.military if salience>1, cl(respondent) 
foreach x of numlist 1(1)2 {
lincom  `x'.military 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.military"
matrix resmat = nullmat(resmat) \ coef
}
*democracy
reg country i.democracy if salience>1, cl(respondent) 
foreach x of numlist 1(1)3 {
lincom  `x'.democracy 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.democracy"
matrix resmat = nullmat(resmat) \ coef
}

*religion
reg country i.religion if salience>1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.religion 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.religion"
matrix resmat = nullmat(resmat) \ coef
}
*Culture (lunar year)
reg country i.culture if salience>1, cl(respondent) 

foreach x of numlist 1(1)2 {
lincom  `x'.culture 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.culture"
matrix resmat = nullmat(resmat) \ coef
}

*economy size
reg country i.econ_size if salience>1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.econ_size
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.econ_size"
matrix resmat = nullmat(resmat) \ coef
}
*distance
reg country i.distance if salience>1, cl(respondent)
foreach x of numlist 1(1)3 {
lincom  `x'.distance 
mat    coef = r(estimate) , r(se)
matrix rownames coef = 	"`x'.distance"
matrix resmat = nullmat(resmat) \ coef
}


mat2txt , matrix(resmat) saving("Nicaragua_salience_low.txt") replace
matrix drop resmat


**graph

insheet using "Nicaragua_salience_low.txt",  clear


gen varname=_n
list


#delimit;
label define varlab 
1"Lower labor standards" 2 "Similar labor standards" 3 "Higher labor standards"
4 "Lower environmental standards" 5 "Similar environmental standards" 6 "Higher environmental standards"
7 "Military ally" 8 "No military ally" 
9 "Autocratic" 10 "Semi-democratic" 11 "Democratic"
12 "Islam" 13 "Diverse" 14 "Christian"
15 "Same culture" 16 "Different culture" 
17 "Smaller econonomic size" 18 "Same economic size" 19 "Larger economic size"
20 "1 000 km" 21 "5 000 km" 22 "10 000 km" ;


#delimit cr
label values varname varlab
list

 
gen clo=c1-1.96*c2
gen chi=c1+1.96*c2


#delimit;
twoway (rspike clo chi varname, horizontal ytitle("") ylabel(1(1)22,valuelabel angle(0)) xlabel(-.1(.1).3) xtitle("Change in Pr (Country preferred as partner)") 
xline(0, lcolor(black))) (scatter varname c1, mcolor(black) msymbol(circle) msize(small) ytitle("")), legend(off);
#delimit cr

graph export "Nicaragua_salience_low.pdf", replace




